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PayPal·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

PayPal data scientist interview with a live Python coding exercise where they had you write bigram generation from scratch and then keep refactoring it down to a one-liner. Pretty focused session, no fluff.

Questions Asked (1)

Q1

Write a Python function that takes a sentence and returns all consecutive word bigrams as a list, then refactor it step by step into a list comprehension and finally a single line using zip.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Started fine with the naive loop version but then they asked me to compress it further and I kind of stalled on the one-liner.

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AI HintsAI Generated

Suggested Approach

Start by writing a clear, readable function using a loop to build the bigrams, then refactor it step by step into a list comprehension and finally a one-liner using zip. Explain each transformation and the trade-offs in readability, performance, and memory usage.

Pro tip: Mention that zip(sentence.split(), sentence.split()[1:]) creates bigrams efficiently, but be aware it creates two lists; for large sentences, consider using itertools.tee or a generator to avoid duplication.

1. Clarify requirements and edge cases

Confirm that bigrams are consecutive word pairs, and discuss handling of empty or single-word sentences (return empty list).

2. Implement with a loop

Write a function that splits the sentence into words, iterates through indices, and appends tuples of consecutive words to a list.

3. Refactor to list comprehension

Replace the loop with a list comprehension that generates bigrams using indexing, e.g., [(words[i], words[i+1]) for i in range(len(words)-1)].

4. Simplify with zip

Use zip(words, words[1:]) to create bigrams in a single line, and explain how zip pairs elements from two iterables.

5. Discuss trade-offs and optimizations

Compare readability, performance, and memory usage of each approach; mention alternatives like using itertools.tee or a generator for large inputs.

Key Points to Mention

  • Definition of bigrams and their use in NLP and text analysis
  • Edge cases: empty string, single word, punctuation handling
  • Time and space complexity of each approach
  • Readability vs. conciseness trade-off
  • Memory efficiency: list slicing creates a copy, zip creates tuples
  • Alternative using itertools.tee or generator expressions for large data

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.